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      "source": [
        "## Importing necessary libraries \n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "execution_count": 1,
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        "outputId": "a77ba090-ba0d-49e7-84de-26b63ccdc6b4"
      },
      "source": [
        "## Installing our favourite pycaret library\n",
        "!pip install pycaret"
      ],
      "execution_count": 14,
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          "text": [
            "Collecting pycaret\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/91/ae/000d825af8f7d9ff86808600f220e7ad57a873987fd6119c87dc4c5b1d91/pycaret-2.0-py3-none-any.whl (255kB)\n",
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            "\u001b[?25hCollecting lightgbm>=2.3.1\n",
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            "  Downloading https://files.pythonhosted.org/packages/0c/4f/29524c9ca35d2ba1a8a3c6c895b90fc92525cf0fe357f747133890953ebe/datefinder-0.7.1-py2.py3-none-any.whl\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.6/dist-packages (from pycaret) (3.2.5)\n",
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            "Collecting pyod\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/77/4e/5767edaccbfc227914ca774cb6ca9b628a08cbb59b9b4839296953a63d34/pyod-0.8.1.tar.gz (93kB)\n",
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            "Collecting kmodes>=0.10.1\n",
            "  Downloading https://files.pythonhosted.org/packages/b2/55/d8ec1ae1f7e1e202a8a4184c6852a3ee993b202b0459672c699d0ac18fc8/kmodes-0.10.2-py2.py3-none-any.whl\n",
            "Collecting pyLDAvis\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a5/3a/af82e070a8a96e13217c8f362f9a73e82d61ac8fff3a2561946a97f96266/pyLDAvis-2.1.2.tar.gz (1.6MB)\n",
            "\u001b[K     |████████████████████████████████| 1.6MB 13.0MB/s \n",
            "\u001b[?25hRequirement already satisfied: xgboost>=0.90 in /usr/local/lib/python3.6/dist-packages (from pycaret) (0.90)\n",
            "Requirement already satisfied: IPython in /usr/local/lib/python3.6/dist-packages (from pycaret) (5.5.0)\n",
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            "Requirement already satisfied: joblib in /usr/local/lib/python3.6/dist-packages (from pycaret) (0.16.0)\n",
            "Collecting mlflow\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/00/2f/2529268d85af0a1521b0b7c137b63b731dff4784e1322fb3055403a959fb/mlflow-1.10.0-py3-none-any.whl (12.4MB)\n",
            "\u001b[K     |████████████████████████████████| 12.4MB 22.0MB/s \n",
            "\u001b[?25hCollecting yellowbrick>=1.0.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/13/95/a14e4fdfb8b1c8753bbe74a626e910a98219ef9c87c6763585bbd30d84cf/yellowbrick-1.1-py3-none-any.whl (263kB)\n",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ojSqYgIZf7s",
        "colab_type": "text"
      },
      "source": [
        "## Dataset Introduction:\n",
        "Cardiotocography\n",
        "Since we all know that there are numerous techniques available to observe the fetus and ultrasound technique is one of the common ones but this ultrasound technique is not very helpful to record the heart-rate of the fetus and other details such as uterine contractions. This is where the cardiotocography comes into play. Cardiotocography is the technique that helps doctors to trace the heart rate of the fetus, which includes measuring accelerations, decelerations, and variability, with the help of uterine contractions. Further, this cardiotocography can be used to classify fetus into three states namely:\n",
        "* Normal trace\n",
        "* Suspicious trace\n",
        "* Pathological trace\n",
        "\n",
        "## Problem Statement\n",
        "Fetal Pulse Rate and Uterine Contractions (UC) are among the basic and common diagnostic techniques to judge maternal and fetal well-being during pregnancy and before delivery. By observing the Cardiotocography data doctors can predict and observe the state of the fetus. Therefore we’ll use CTG data  to predict the state of the fetus. \n",
        "\n",
        "Dataset link: https://www.kaggle.com/akshat0007/fetalhr"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1pkwzPRqORn-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## Reading the dataset using pandas\n",
        "df=pd.read_csv(\"CTG.csv\")"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nfMRHcmwORoC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 216
        },
        "outputId": "5f96bdfc-07b6-40fc-df7c-3c38652ef15c"
      },
      "source": [
        "## Having a look of our data\n",
        "df.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>0.5</td>\n",
              "      <td>43.0</td>\n",
              "      <td>2.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>62.0</td>\n",
              "      <td>126.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>137.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Fmcs_1.txt</td>\n",
              "      <td>5/3/1996</td>\n",
              "      <td>CTG0002.txt</td>\n",
              "      <td>5.0</td>\n",
              "      <td>632.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>2.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>198.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>141.0</td>\n",
              "      <td>136.0</td>\n",
              "      <td>140.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Fmcs_1.txt</td>\n",
              "      <td>5/3/1996</td>\n",
              "      <td>CTG0003.txt</td>\n",
              "      <td>177.0</td>\n",
              "      <td>779.0</td>\n",
              "      <td>133.0</td>\n",
              "      <td>133.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>2.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>13.4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>198.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>141.0</td>\n",
              "      <td>135.0</td>\n",
              "      <td>138.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Fmcs_1.txt</td>\n",
              "      <td>5/3/1996</td>\n",
              "      <td>CTG0004.txt</td>\n",
              "      <td>411.0</td>\n",
              "      <td>1192.0</td>\n",
              "      <td>134.0</td>\n",
              "      <td>134.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>2.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>117.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>170.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>137.0</td>\n",
              "      <td>134.0</td>\n",
              "      <td>137.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Fmcs_1.txt</td>\n",
              "      <td>5/3/1996</td>\n",
              "      <td>CTG0005.txt</td>\n",
              "      <td>533.0</td>\n",
              "      <td>1147.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>2.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>19.9</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>117.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>170.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>137.0</td>\n",
              "      <td>136.0</td>\n",
              "      <td>138.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       FileName       Date      SegFile      b  ...   FS  SUSP  CLASS  NSP\n",
              "0  Variab10.txt  12/1/1996  CTG0001.txt  240.0  ...  1.0   0.0    9.0  2.0\n",
              "1    Fmcs_1.txt   5/3/1996  CTG0002.txt    5.0  ...  0.0   0.0    6.0  1.0\n",
              "2    Fmcs_1.txt   5/3/1996  CTG0003.txt  177.0  ...  0.0   0.0    6.0  1.0\n",
              "3    Fmcs_1.txt   5/3/1996  CTG0004.txt  411.0  ...  0.0   0.0    6.0  1.0\n",
              "4    Fmcs_1.txt   5/3/1996  CTG0005.txt  533.0  ...  0.0   0.0    2.0  1.0\n",
              "\n",
              "[5 rows x 40 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hc0WTN3jORoF",
        "colab_type": "text"
      },
      "source": [
        "# Feature Abbreviations used in the dataset :-\n",
        "\n",
        "\n",
        "\n",
        "### FileName:\tof CTG examination\t\n",
        "### Date:\tof the examination\t\n",
        "### b:\tstart instant\t\n",
        "### e:\tend instant\t\n",
        "### LBE:\tbaseline value (medical expert)\t\n",
        "### LB:\tbaseline value (SisPorto)\t\n",
        "### AC:\taccelerations (SisPorto)\t\n",
        "### FM:\tfoetal movement (SisPorto)\t\n",
        "### UC:\tuterine contractions (SisPorto)\t\n",
        "### ASTV:\tpercentage of time with abnormal short term variability  (SisPorto)\t\n",
        "### mSTV:\tmean value of short term variability  (SisPorto)\t\n",
        "### ALTV:\tpercentage of time with abnormal long term variability  (SisPorto)\t\n",
        "### mLTV:\tmean value of long term variability  (SisPorto)\t\n",
        "### DL:\tlight decelerations\t\n",
        "### DS:\tsevere decelerations\t\n",
        "### DP:\tprolongued decelerations\t\n",
        "### DR:\trepetitive decelerations\t\n",
        "### Width:\thistogram width\t\n",
        "### Min:\tlow freq. of the histogram\t\n",
        "### Max:\thigh freq. of the histogram\t\n",
        "### Nmax:\tnumber of histogram peaks\t\n",
        "### Nzeros:\tnumber of histogram zeros\t\n",
        "### Mode:\thistogram mode\t\n",
        "### Mean:\thistogram mean\t\n",
        "### Median:\thistogram median\t\n",
        "### Variance:\thistogram variance\t\n",
        "### Tendency:\thistogram tendency: -1=left assymetric; 0=symmetric; 1=right assymetric\t\n",
        "### A:\tcalm sleep\t\n",
        "### B:\tREM sleep\t\n",
        "### C:\tcalm vigilance\t\n",
        "### D:\tactive vigilance\t\n",
        "### SH:\tshift pattern (A or Susp with shifts)\t\n",
        "### AD:\taccelerative/decelerative pattern (stress situation)\t\n",
        "### DE:\tdecelerative pattern (vagal stimulation)\t\n",
        "### LD:\tlargely decelerative pattern\t\n",
        "### FS:\tflat-sinusoidal pattern (pathological state)\t\n",
        "### SUSP:\tsuspect pattern\t\n",
        "### CLASS:\tClass code (1 to 10) for classes A to SUSP\t\n",
        "### NSP:\tNormal=1; Suspect=2; Pathologic=3\t\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qS3F3sS9ORoG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## Dropping the columns which we don't need\n",
        "df=df.drop([\"FileName\",\"Date\",\"SegFile\",\"b\",\"e\"],axis=1)"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8_j3dXzCORoI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 216
        },
        "outputId": "97ba0c46-aeb1-4170-be52-52bbfbc52df1"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>LBE</th>\n",
              "      <th>LB</th>\n",
              "      <th>AC</th>\n",
              "      <th>FM</th>\n",
              "      <th>UC</th>\n",
              "      <th>ASTV</th>\n",
              "      <th>MSTV</th>\n",
              "      <th>ALTV</th>\n",
              "      <th>MLTV</th>\n",
              "      <th>DL</th>\n",
              "      <th>DS</th>\n",
              "      <th>DP</th>\n",
              "      <th>DR</th>\n",
              "      <th>Width</th>\n",
              "      <th>Min</th>\n",
              "      <th>Max</th>\n",
              "      <th>Nmax</th>\n",
              "      <th>Nzeros</th>\n",
              "      <th>Mode</th>\n",
              "      <th>Mean</th>\n",
              "      <th>Median</th>\n",
              "      <th>Variance</th>\n",
              "      <th>Tendency</th>\n",
              "      <th>A</th>\n",
              "      <th>B</th>\n",
              "      <th>C</th>\n",
              "      <th>D</th>\n",
              "      <th>E</th>\n",
              "      <th>AD</th>\n",
              "      <th>DE</th>\n",
              "      <th>LD</th>\n",
              "      <th>FS</th>\n",
              "      <th>SUSP</th>\n",
              "      <th>CLASS</th>\n",
              "      <th>NSP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>120.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>43.0</td>\n",
              "      <td>2.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>62.0</td>\n",
              "      <td>126.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>137.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>132.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>2.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>198.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>141.0</td>\n",
              "      <td>136.0</td>\n",
              "      <td>140.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>133.0</td>\n",
              "      <td>133.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>2.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>13.4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>198.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>141.0</td>\n",
              "      <td>135.0</td>\n",
              "      <td>138.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>134.0</td>\n",
              "      <td>134.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>2.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>117.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>170.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>137.0</td>\n",
              "      <td>134.0</td>\n",
              "      <td>137.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>132.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>2.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>19.9</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>117.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>170.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>137.0</td>\n",
              "      <td>136.0</td>\n",
              "      <td>138.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     LBE     LB   AC   FM   UC  ASTV  ...   DE   LD   FS  SUSP  CLASS  NSP\n",
              "0  120.0  120.0  0.0  0.0  0.0  73.0  ...  0.0  0.0  1.0   0.0    9.0  2.0\n",
              "1  132.0  132.0  4.0  0.0  4.0  17.0  ...  0.0  0.0  0.0   0.0    6.0  1.0\n",
              "2  133.0  133.0  2.0  0.0  5.0  16.0  ...  0.0  0.0  0.0   0.0    6.0  1.0\n",
              "3  134.0  134.0  2.0  0.0  6.0  16.0  ...  0.0  0.0  0.0   0.0    6.0  1.0\n",
              "4  132.0  132.0  4.0  0.0  5.0  16.0  ...  0.0  0.0  0.0   0.0    2.0  1.0\n",
              "\n",
              "[5 rows x 35 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2lTTiQ0LORoL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        },
        "outputId": "cebda70b-1bcd-493d-d00a-d73857a37c5b"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['LBE', 'LB', 'AC', 'FM', 'UC', 'ASTV', 'MSTV', 'ALTV', 'MLTV', 'DL',\n",
              "       'DS', 'DP', 'DR', 'Width', 'Min', 'Max', 'Nmax', 'Nzeros', 'Mode',\n",
              "       'Mean', 'Median', 'Variance', 'Tendency', 'A', 'B', 'C', 'D', 'E', 'AD',\n",
              "       'DE', 'LD', 'FS', 'SUSP', 'CLASS', 'NSP'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gK6s6aXbRxlZ",
        "colab_type": "text"
      },
      "source": [
        "## Performing some basic preprocessing techniques"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W8hRcAhlORoQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "ab1046ff-8419-4aa2-a36c-2bcc92c759d3"
      },
      "source": [
        "## This will print the number of columns and rows\n",
        "print(df.shape)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(2126, 35)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QW-KthkFORoT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        },
        "outputId": "1ee8f939-b347-45c0-91cb-cb9d24422e3b"
      },
      "source": [
        "## Checking for the null values\n",
        "df.isnull().sum()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LBE         3\n",
              "LB          3\n",
              "AC          3\n",
              "FM          2\n",
              "UC          2\n",
              "ASTV        2\n",
              "MSTV        2\n",
              "ALTV        2\n",
              "MLTV        2\n",
              "DL          1\n",
              "DS          1\n",
              "DP          1\n",
              "DR          1\n",
              "Width       3\n",
              "Min         3\n",
              "Max         3\n",
              "Nmax        3\n",
              "Nzeros      3\n",
              "Mode        3\n",
              "Mean        3\n",
              "Median      3\n",
              "Variance    3\n",
              "Tendency    3\n",
              "A           3\n",
              "B           3\n",
              "C           3\n",
              "D           3\n",
              "E           3\n",
              "AD          3\n",
              "DE          3\n",
              "LD          3\n",
              "FS          3\n",
              "SUSP        3\n",
              "CLASS       3\n",
              "NSP         3\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mJjIfadIORoW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## Dropping the the rows containing null values\n",
        "df=df.dropna()"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VojGGVKZORoY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        },
        "outputId": "528a202a-4a5a-43f2-c62c-604a5d7efb89"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LBE         0\n",
              "LB          0\n",
              "AC          0\n",
              "FM          0\n",
              "UC          0\n",
              "ASTV        0\n",
              "MSTV        0\n",
              "ALTV        0\n",
              "MLTV        0\n",
              "DL          0\n",
              "DS          0\n",
              "DP          0\n",
              "DR          0\n",
              "Width       0\n",
              "Min         0\n",
              "Max         0\n",
              "Nmax        0\n",
              "Nzeros      0\n",
              "Mode        0\n",
              "Mean        0\n",
              "Median      0\n",
              "Variance    0\n",
              "Tendency    0\n",
              "A           0\n",
              "B           0\n",
              "C           0\n",
              "D           0\n",
              "E           0\n",
              "AD          0\n",
              "DE          0\n",
              "LD          0\n",
              "FS          0\n",
              "SUSP        0\n",
              "CLASS       0\n",
              "NSP         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6w2Qld4GORoa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        },
        "outputId": "317d255a-6a28-45b4-d6d4-9a7c4681ccd3"
      },
      "source": [
        "## Checking the data type of the columns\n",
        "df.dtypes"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LBE         float64\n",
              "LB          float64\n",
              "AC          float64\n",
              "FM          float64\n",
              "UC          float64\n",
              "ASTV        float64\n",
              "MSTV        float64\n",
              "ALTV        float64\n",
              "MLTV        float64\n",
              "DL          float64\n",
              "DS          float64\n",
              "DP          float64\n",
              "DR          float64\n",
              "Width       float64\n",
              "Min         float64\n",
              "Max         float64\n",
              "Nmax        float64\n",
              "Nzeros      float64\n",
              "Mode        float64\n",
              "Mean        float64\n",
              "Median      float64\n",
              "Variance    float64\n",
              "Tendency    float64\n",
              "A           float64\n",
              "B           float64\n",
              "C           float64\n",
              "D           float64\n",
              "E           float64\n",
              "AD          float64\n",
              "DE          float64\n",
              "LD          float64\n",
              "FS          float64\n",
              "SUSP        float64\n",
              "CLASS       float64\n",
              "NSP         float64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vCVQdQ9ZR3Mv",
        "colab_type": "text"
      },
      "source": [
        "## Importing the pycaret library"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7oVTEnMxORod",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# This command will basically import all the modules from pycaret that are necessary for classification tasks\n",
        "from pycaret.classification import *"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mmTt0qoaPb8B",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 918,
          "referenced_widgets": [
            "85013890037d4eae9e46bce44ef6fe31",
            "d1726335eb9847dcb8c408db31d29e39",
            "c069b042d8ee4808a04e5a02388dbc89",
            "8b11b69c85564a5fbd8a7656ff91e850",
            "2016bba888b7450f96b6018d8100e534",
            "d82f871fd9494cd29198d0758cc3ddde"
          ]
        },
        "outputId": "9d1c6476-fffd-4d78-c678-b3a8c9ed1ace"
      },
      "source": [
        "# Setting up the classifier\n",
        "# Pass the complete dataset as data and the featured to be predicted as target\n",
        "clf=setup(data=df,target='NSP')"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Setup Succesfully Completed!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style  type=\"text/css\" >\n",
              "</style><table id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Description</th>        <th class=\"col_heading level0 col1\" >Value</th>    </tr></thead><tbody>\n",
              "                <tr>\n",
              "                        <th id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row0_col0\" class=\"data row0 col0\" >session_id</td>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row0_col1\" class=\"data row0 col1\" >4471</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row1_col0\" class=\"data row1 col0\" >Target Type</td>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row1_col1\" class=\"data row1 col1\" >Multiclass</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row2_col0\" class=\"data row2 col0\" >Label Encoded</td>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row2_col1\" class=\"data row2 col1\" >None</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row3_col0\" class=\"data row3 col0\" >Original Data</td>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row3_col1\" class=\"data row3 col1\" >(2126, 35)</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row4_col0\" class=\"data row4 col0\" >Missing Values </td>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row4_col1\" class=\"data row4 col1\" >False</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row5_col0\" class=\"data row5 col0\" >Numeric Features </td>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row5_col1\" class=\"data row5 col1\" >23</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row6_col0\" class=\"data row6 col0\" >Categorical Features </td>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row6_col1\" class=\"data row6 col1\" >11</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row7_col0\" class=\"data row7 col0\" >Ordinal Features </td>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row7_col1\" class=\"data row7 col1\" >False</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row8_col0\" class=\"data row8 col0\" >High Cardinality Features </td>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row8_col1\" class=\"data row8 col1\" >False</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row9_col0\" class=\"data row9 col0\" >High Cardinality Method </td>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row9_col1\" class=\"data row9 col1\" >None</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row10_col0\" class=\"data row10 col0\" >Sampled Data</td>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row10_col1\" class=\"data row10 col1\" >(2126, 35)</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row11_col0\" class=\"data row11 col0\" >Transformed Train Set</td>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row11_col1\" class=\"data row11 col1\" >(1488, 45)</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row12_col0\" class=\"data row12 col0\" >Transformed Test Set</td>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row12_col1\" class=\"data row12 col1\" >(638, 45)</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row13_col0\" class=\"data row13 col0\" >Numeric Imputer </td>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row13_col1\" class=\"data row13 col1\" >mean</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row14_col0\" class=\"data row14 col0\" >Categorical Imputer </td>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row14_col1\" class=\"data row14 col1\" >constant</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row15_col0\" class=\"data row15 col0\" >Normalize </td>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row15_col1\" class=\"data row15 col1\" >False</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row16_col0\" class=\"data row16 col0\" >Normalize Method </td>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row16_col1\" class=\"data row16 col1\" >None</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row17_col0\" class=\"data row17 col0\" >Transformation </td>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row17_col1\" class=\"data row17 col1\" >False</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row18_col0\" class=\"data row18 col0\" >Transformation Method </td>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row18_col1\" class=\"data row18 col1\" >None</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row19_col0\" class=\"data row19 col0\" >PCA </td>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row19_col1\" class=\"data row19 col1\" >False</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row20_col0\" class=\"data row20 col0\" >PCA Method </td>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row20_col1\" class=\"data row20 col1\" >None</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row21_col0\" class=\"data row21 col0\" >PCA Components </td>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row21_col1\" class=\"data row21 col1\" >None</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row22_col0\" class=\"data row22 col0\" >Ignore Low Variance </td>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row22_col1\" class=\"data row22 col1\" >False</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row23_col0\" class=\"data row23 col0\" >Combine Rare Levels </td>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row23_col1\" class=\"data row23 col1\" >False</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row24_col0\" class=\"data row24 col0\" >Rare Level Threshold </td>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row24_col1\" class=\"data row24 col1\" >None</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row25_col0\" class=\"data row25 col0\" >Numeric Binning </td>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row25_col1\" class=\"data row25 col1\" >False</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row26_col0\" class=\"data row26 col0\" >Remove Outliers </td>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row26_col1\" class=\"data row26 col1\" >False</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row27_col0\" class=\"data row27 col0\" >Outliers Threshold </td>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row27_col1\" class=\"data row27 col1\" >None</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row28_col0\" class=\"data row28 col0\" >Remove Multicollinearity </td>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row28_col1\" class=\"data row28 col1\" >False</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row29_col0\" class=\"data row29 col0\" >Multicollinearity Threshold </td>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row29_col1\" class=\"data row29 col1\" >None</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row30_col0\" class=\"data row30 col0\" >Clustering </td>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row30_col1\" class=\"data row30 col1\" >False</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row31_col0\" class=\"data row31 col0\" >Clustering Iteration </td>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row31_col1\" class=\"data row31 col1\" >None</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row32_col0\" class=\"data row32 col0\" >Polynomial Features </td>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row32_col1\" class=\"data row32 col1\" >False</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002level0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row33_col0\" class=\"data row33 col0\" >Polynomial Degree </td>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row33_col1\" class=\"data row33 col1\" >None</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002level0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row34_col0\" class=\"data row34 col0\" >Trignometry Features </td>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row34_col1\" class=\"data row34 col1\" >False</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002level0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row35_col0\" class=\"data row35 col0\" >Polynomial Threshold </td>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row35_col1\" class=\"data row35 col1\" >None</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002level0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row36_col0\" class=\"data row36 col0\" >Group Features </td>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row36_col1\" class=\"data row36 col1\" >False</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002level0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row37_col0\" class=\"data row37 col0\" >Feature Selection </td>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row37_col1\" class=\"data row37 col1\" >False</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002level0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row38_col0\" class=\"data row38 col0\" >Features Selection Threshold </td>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row38_col1\" class=\"data row38 col1\" >None</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002level0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row39_col0\" class=\"data row39 col0\" >Feature Interaction </td>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row39_col1\" class=\"data row39 col1\" >False</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002level0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row40_col0\" class=\"data row40 col0\" >Feature Ratio </td>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row40_col1\" class=\"data row40 col1\" >False</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002level0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row41_col0\" class=\"data row41 col0\" >Interaction Threshold </td>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row41_col1\" class=\"data row41 col1\" >None</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002level0_row42\" class=\"row_heading level0 row42\" >42</th>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row42_col0\" class=\"data row42 col0\" >Fix Imbalance</td>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row42_col1\" class=\"data row42 col1\" >False</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002level0_row43\" class=\"row_heading level0 row43\" >43</th>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row43_col0\" class=\"data row43 col0\" >Fix Imbalance Method</td>\n",
              "                        <td id=\"T_98f39b8c_d7f5_11ea_84df_0242ac1c0002row43_col1\" class=\"data row43 col1\" >SMOTE</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f81a1f66f98>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5c5D_CcpP4pR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 633,
          "referenced_widgets": [
            "04d55d6a1d1a482a9b7009586b58979b",
            "f09f1960e4254302a99b590ce733d608",
            "fff4b352b5594f77bf468695d468f288"
          ]
        },
        "outputId": "a4a72143-5ccf-4429-f9cd-af1da1210ab0"
      },
      "source": [
        "# This model will be used to compare all the model along with the cross validation\n",
        "compare_models()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "            text-align:  left;\n",
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              "            text-align:  left;\n",
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              "            : ;\n",
              "            text-align:  left;\n",
              "        }    #T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row5_col2 {\n",
              "            text-align:  left;\n",
              "        }    #T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row5_col3 {\n",
              "            : ;\n",
              "            text-align:  left;\n",
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              "            : ;\n",
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              "            : ;\n",
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              "            : ;\n",
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              "            background-color:  lightgrey;\n",
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              "            text-align:  left;\n",
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              "            : ;\n",
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              "            text-align:  left;\n",
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              "            : ;\n",
              "            text-align:  left;\n",
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              "            : ;\n",
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              "            text-align:  left;\n",
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              "            text-align:  left;\n",
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              "            text-align:  left;\n",
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              "            text-align:  left;\n",
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              "            : ;\n",
              "            text-align:  left;\n",
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              "            : ;\n",
              "            text-align:  left;\n",
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              "            : ;\n",
              "            text-align:  left;\n",
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              "            : ;\n",
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              "            text-align:  left;\n",
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              "            : ;\n",
              "            text-align:  left;\n",
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              "            text-align:  left;\n",
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              "            : ;\n",
              "            text-align:  left;\n",
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              "            : ;\n",
              "            text-align:  left;\n",
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              "            : ;\n",
              "            text-align:  left;\n",
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              "            : ;\n",
              "            text-align:  left;\n",
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              "            : ;\n",
              "            text-align:  left;\n",
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              "            text-align:  left;\n",
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              "            : ;\n",
              "            text-align:  left;\n",
              "        }    #T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row14_col2 {\n",
              "            text-align:  left;\n",
              "        }    #T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row14_col3 {\n",
              "            : ;\n",
              "            text-align:  left;\n",
              "        }    #T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row14_col4 {\n",
              "            : ;\n",
              "            text-align:  left;\n",
              "        }    #T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row14_col5 {\n",
              "            : ;\n",
              "            text-align:  left;\n",
              "        }    #T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row14_col6 {\n",
              "            : ;\n",
              "            text-align:  left;\n",
              "        }    #T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row14_col7 {\n",
              "            : ;\n",
              "            text-align:  left;\n",
              "        }    #T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row14_col8 {\n",
              "            background-color:  lightgrey;\n",
              "            text-align:  left;\n",
              "        }</style><table id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Model</th>        <th class=\"col_heading level0 col1\" >Accuracy</th>        <th class=\"col_heading level0 col2\" >AUC</th>        <th class=\"col_heading level0 col3\" >Recall</th>        <th class=\"col_heading level0 col4\" >Prec.</th>        <th class=\"col_heading level0 col5\" >F1</th>        <th class=\"col_heading level0 col6\" >Kappa</th>        <th class=\"col_heading level0 col7\" >MCC</th>        <th class=\"col_heading level0 col8\" >TT (Sec)</th>    </tr></thead><tbody>\n",
              "                <tr>\n",
              "                        <th id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row0_col0\" class=\"data row0 col0\" >Extra Trees Classifier</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row0_col1\" class=\"data row0 col1\" >0.9926</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row0_col2\" class=\"data row0 col2\" >0.0000</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row0_col3\" class=\"data row0 col3\" >0.9850</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row0_col4\" class=\"data row0 col4\" >0.9928</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row0_col5\" class=\"data row0 col5\" >0.9925</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row0_col6\" class=\"data row0 col6\" >0.9795</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row0_col7\" class=\"data row0 col7\" >0.9800</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row0_col8\" class=\"data row0 col8\" >0.6159</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row1_col0\" class=\"data row1 col0\" >Extreme Gradient Boosting</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row1_col1\" class=\"data row1 col1\" >0.9926</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row1_col2\" class=\"data row1 col2\" >0.0000</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row1_col3\" class=\"data row1 col3\" >0.9863</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row1_col4\" class=\"data row1 col4\" >0.9928</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row1_col5\" class=\"data row1 col5\" >0.9925</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row1_col6\" class=\"data row1 col6\" >0.9796</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row1_col7\" class=\"data row1 col7\" >0.9800</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row1_col8\" class=\"data row1 col8\" >0.4475</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row2_col0\" class=\"data row2 col0\" >Random Forest Classifier</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row2_col1\" class=\"data row2 col1\" >0.9913</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row2_col2\" class=\"data row2 col2\" >0.0000</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row2_col3\" class=\"data row2 col3\" >0.9818</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row2_col4\" class=\"data row2 col4\" >0.9914</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row2_col5\" class=\"data row2 col5\" >0.9911</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row2_col6\" class=\"data row2 col6\" >0.9758</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row2_col7\" class=\"data row2 col7\" >0.9763</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row2_col8\" class=\"data row2 col8\" >0.2814</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row3_col0\" class=\"data row3 col0\" >Light Gradient Boosting Machine</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row3_col1\" class=\"data row3 col1\" >0.9913</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row3_col2\" class=\"data row3 col2\" >0.0000</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row3_col3\" class=\"data row3 col3\" >0.9817</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row3_col4\" class=\"data row3 col4\" >0.9915</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row3_col5\" class=\"data row3 col5\" >0.9911</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row3_col6\" class=\"data row3 col6\" >0.9758</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row3_col7\" class=\"data row3 col7\" >0.9763</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row3_col8\" class=\"data row3 col8\" >0.3541</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row4_col0\" class=\"data row4 col0\" >CatBoost Classifier</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row4_col1\" class=\"data row4 col1\" >0.9913</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row4_col2\" class=\"data row4 col2\" >0.0000</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row4_col3\" class=\"data row4 col3\" >0.9818</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row4_col4\" class=\"data row4 col4\" >0.9915</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row4_col5\" class=\"data row4 col5\" >0.9911</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row4_col6\" class=\"data row4 col6\" >0.9758</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row4_col7\" class=\"data row4 col7\" >0.9763</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row4_col8\" class=\"data row4 col8\" >9.7183</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row5_col0\" class=\"data row5 col0\" >Ada Boost Classifier</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row5_col1\" class=\"data row5 col1\" >0.9879</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row5_col2\" class=\"data row5 col2\" >0.0000</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row5_col3\" class=\"data row5 col3\" >0.9777</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row5_col4\" class=\"data row5 col4\" >0.9882</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row5_col5\" class=\"data row5 col5\" >0.9877</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row5_col6\" class=\"data row5 col6\" >0.9665</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row5_col7\" class=\"data row5 col7\" >0.9672</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row5_col8\" class=\"data row5 col8\" >0.4853</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row6_col0\" class=\"data row6 col0\" >Gradient Boosting Classifier</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row6_col1\" class=\"data row6 col1\" >0.9879</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row6_col2\" class=\"data row6 col2\" >0.0000</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row6_col3\" class=\"data row6 col3\" >0.9763</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row6_col4\" class=\"data row6 col4\" >0.9881</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row6_col5\" class=\"data row6 col5\" >0.9877</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row6_col6\" class=\"data row6 col6\" >0.9666</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row6_col7\" class=\"data row6 col7\" >0.9672</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row6_col8\" class=\"data row6 col8\" >1.2628</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row7_col0\" class=\"data row7 col0\" >Ridge Classifier</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row7_col1\" class=\"data row7 col1\" >0.9859</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row7_col2\" class=\"data row7 col2\" >0.0000</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row7_col3\" class=\"data row7 col3\" >0.9689</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row7_col4\" class=\"data row7 col4\" >0.9862</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row7_col5\" class=\"data row7 col5\" >0.9855</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row7_col6\" class=\"data row7 col6\" >0.9604</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row7_col7\" class=\"data row7 col7\" >0.9615</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row7_col8\" class=\"data row7 col8\" >0.0282</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row8_col0\" class=\"data row8 col0\" >Linear Discriminant Analysis</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row8_col1\" class=\"data row8 col1\" >0.9859</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row8_col2\" class=\"data row8 col2\" >0.0000</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row8_col3\" class=\"data row8 col3\" >0.9689</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row8_col4\" class=\"data row8 col4\" >0.9862</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row8_col5\" class=\"data row8 col5\" >0.9855</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row8_col6\" class=\"data row8 col6\" >0.9604</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row8_col7\" class=\"data row8 col7\" >0.9615</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row8_col8\" class=\"data row8 col8\" >0.0674</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row9_col0\" class=\"data row9 col0\" >Decision Tree Classifier</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row9_col1\" class=\"data row9 col1\" >0.9805</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row9_col2\" class=\"data row9 col2\" >0.0000</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row9_col3\" class=\"data row9 col3\" >0.9692</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row9_col4\" class=\"data row9 col4\" >0.9815</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row9_col5\" class=\"data row9 col5\" >0.9805</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row9_col6\" class=\"data row9 col6\" >0.9472</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row9_col7\" class=\"data row9 col7\" >0.9476</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row9_col8\" class=\"data row9 col8\" >0.0355</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row10_col0\" class=\"data row10 col0\" >Logistic Regression</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row10_col1\" class=\"data row10 col1\" >0.9590</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row10_col2\" class=\"data row10 col2\" >0.0000</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row10_col3\" class=\"data row10 col3\" >0.9035</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row10_col4\" class=\"data row10 col4\" >0.9597</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row10_col5\" class=\"data row10 col5\" >0.9578</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row10_col6\" class=\"data row10 col6\" >0.8845</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row10_col7\" class=\"data row10 col7\" >0.8869</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row10_col8\" class=\"data row10 col8\" >0.2037</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row11_col0\" class=\"data row11 col0\" >Naive Bayes</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row11_col1\" class=\"data row11 col1\" >0.9429</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row11_col2\" class=\"data row11 col2\" >0.0000</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row11_col3\" class=\"data row11 col3\" >0.9651</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row11_col4\" class=\"data row11 col4\" >0.9567</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row11_col5\" class=\"data row11 col5\" >0.9463</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row11_col6\" class=\"data row11 col6\" >0.8580</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row11_col7\" class=\"data row11 col7\" >0.8658</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row11_col8\" class=\"data row11 col8\" >0.0160</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row12_col0\" class=\"data row12 col0\" >SVM - Linear Kernel</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row12_col1\" class=\"data row12 col1\" >0.9174</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row12_col2\" class=\"data row12 col2\" >0.0000</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row12_col3\" class=\"data row12 col3\" >0.7901</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row12_col4\" class=\"data row12 col4\" >0.9206</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row12_col5\" class=\"data row12 col5\" >0.9133</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row12_col6\" class=\"data row12 col6\" >0.7621</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row12_col7\" class=\"data row12 col7\" >0.7712</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row12_col8\" class=\"data row12 col8\" >0.0476</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row13_col0\" class=\"data row13 col0\" >K Neighbors Classifier</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row13_col1\" class=\"data row13 col1\" >0.9079</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row13_col2\" class=\"data row13 col2\" >0.0000</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row13_col3\" class=\"data row13 col3\" >0.7949</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row13_col4\" class=\"data row13 col4\" >0.9051</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row13_col5\" class=\"data row13 col5\" >0.9039</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row13_col6\" class=\"data row13 col6\" >0.7351</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row13_col7\" class=\"data row13 col7\" >0.7402</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row13_col8\" class=\"data row13 col8\" >0.0295</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row14_col0\" class=\"data row14 col0\" >Quadratic Discriminant Analysis</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row14_col1\" class=\"data row14 col1\" >0.8804</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row14_col2\" class=\"data row14 col2\" >0.0000</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row14_col3\" class=\"data row14 col3\" >0.9244</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row14_col4\" class=\"data row14 col4\" >0.9366</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row14_col5\" class=\"data row14 col5\" >0.8944</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row14_col6\" class=\"data row14 col6\" >0.7415</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row14_col7\" class=\"data row14 col7\" >0.7697</td>\n",
              "                        <td id=\"T_43f8a9ec_d7f5_11ea_84df_0242ac1c0002row14_col8\" class=\"data row14 col8\" >0.0312</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f81a2333dd8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OneVsRestClassifier(estimator=ExtraTreesClassifier(bootstrap=False,\n",
              "                                                   ccp_alpha=0.0,\n",
              "                                                   class_weight=None,\n",
              "                                                   criterion='gini',\n",
              "                                                   max_depth=None,\n",
              "                                                   max_features='auto',\n",
              "                                                   max_leaf_nodes=None,\n",
              "                                                   max_samples=None,\n",
              "                                                   min_impurity_decrease=0.0,\n",
              "                                                   min_impurity_split=None,\n",
              "                                                   min_samples_leaf=1,\n",
              "                                                   min_samples_split=2,\n",
              "                                                   min_weight_fraction_leaf=0.0,\n",
              "                                                   n_estimators=100, n_jobs=-1,\n",
              "                                                   oob_score=False,\n",
              "                                                   random_state=8450, verbose=0,\n",
              "                                                   warm_start=False),\n",
              "                    n_jobs=-1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jlbJqN8fS_fb",
        "colab_type": "text"
      },
      "source": [
        "### The AUC score is 0.000 because it is not supported for the muli-classification tasks\n",
        "\n",
        "### Also, from the above it is understood that Extreme Gradient Boosting(popularly known as XGBoost) model really performed well. So, we will proceed with Extreme Gradient Boosting model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fXcVr-yCTtBx",
        "colab_type": "text"
      },
      "source": [
        "## Creating the Extreme Gradient Boosting(XGBoost) model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rvy2ZevaRQB4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273,
          "referenced_widgets": [
            "1deea41a50a34193a4a1d4a53ef32286",
            "aa38ef63b6d4402fabe4edb0d6419168",
            "ed7d18dacd964948995dc160f99736cf"
          ]
        },
        "outputId": "05eac637-6b6a-45f4-cdfd-1d9e15008faf"
      },
      "source": [
        "xgboost_classifier=create_model('xgboost')"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style  type=\"text/css\" >\n",
              "    #T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row10_col0 {\n",
              "            background:  yellow;\n",
              "        }    #T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row10_col1 {\n",
              "            background:  yellow;\n",
              "        }    #T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row10_col2 {\n",
              "            background:  yellow;\n",
              "        }    #T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row10_col3 {\n",
              "            background:  yellow;\n",
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              "            background:  yellow;\n",
              "        }    #T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row10_col5 {\n",
              "            background:  yellow;\n",
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              "            background:  yellow;\n",
              "        }</style><table id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Accuracy</th>        <th class=\"col_heading level0 col1\" >AUC</th>        <th class=\"col_heading level0 col2\" >Recall</th>        <th class=\"col_heading level0 col3\" >Prec.</th>        <th class=\"col_heading level0 col4\" >F1</th>        <th class=\"col_heading level0 col5\" >Kappa</th>        <th class=\"col_heading level0 col6\" >MCC</th>    </tr></thead><tbody>\n",
              "                <tr>\n",
              "                        <th id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row0_col0\" class=\"data row0 col0\" >0.9866</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row0_col1\" class=\"data row0 col1\" >0.0000</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row0_col2\" class=\"data row0 col2\" >0.9683</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row0_col3\" class=\"data row0 col3\" >0.9868</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row0_col4\" class=\"data row0 col4\" >0.9863</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row0_col5\" class=\"data row0 col5\" >0.9626</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row0_col6\" class=\"data row0 col6\" >0.9634</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row1_col0\" class=\"data row1 col0\" >1.0000</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row1_col1\" class=\"data row1 col1\" >0.0000</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row1_col2\" class=\"data row1 col2\" >1.0000</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row1_col3\" class=\"data row1 col3\" >1.0000</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row1_col4\" class=\"data row1 col4\" >1.0000</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row1_col5\" class=\"data row1 col5\" >1.0000</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row1_col6\" class=\"data row1 col6\" >1.0000</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row2_col0\" class=\"data row2 col0\" >0.9799</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row2_col1\" class=\"data row2 col1\" >0.0000</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row2_col2\" class=\"data row2 col2\" >0.9524</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row2_col3\" class=\"data row2 col3\" >0.9804</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row2_col4\" class=\"data row2 col4\" >0.9792</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row2_col5\" class=\"data row2 col5\" >0.9432</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row2_col6\" class=\"data row2 col6\" >0.9450</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row3_col0\" class=\"data row3 col0\" >0.9933</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row3_col1\" class=\"data row3 col1\" >0.0000</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row3_col2\" class=\"data row3 col2\" >0.9841</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row3_col3\" class=\"data row3 col3\" >0.9938</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row3_col4\" class=\"data row3 col4\" >0.9933</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row3_col5\" class=\"data row3 col5\" >0.9818</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row3_col6\" class=\"data row3 col6\" >0.9819</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row4_col0\" class=\"data row4 col0\" >1.0000</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row4_col1\" class=\"data row4 col1\" >0.0000</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row4_col2\" class=\"data row4 col2\" >1.0000</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row4_col3\" class=\"data row4 col3\" >1.0000</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row4_col4\" class=\"data row4 col4\" >1.0000</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row4_col5\" class=\"data row4 col5\" >1.0000</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row4_col6\" class=\"data row4 col6\" >1.0000</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row5_col0\" class=\"data row5 col0\" >0.9799</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row5_col1\" class=\"data row5 col1\" >0.0000</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row5_col2\" class=\"data row5 col2\" >0.9654</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row5_col3\" class=\"data row5 col3\" >0.9796</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row5_col4\" class=\"data row5 col4\" >0.9797</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row5_col5\" class=\"data row5 col5\" >0.9460</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row5_col6\" class=\"data row5 col6\" >0.9461</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row6_col0\" class=\"data row6 col0\" >0.9933</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row6_col1\" class=\"data row6 col1\" >0.0000</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row6_col2\" class=\"data row6 col2\" >0.9971</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row6_col3\" class=\"data row6 col3\" >0.9936</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row6_col4\" class=\"data row6 col4\" >0.9934</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row6_col5\" class=\"data row6 col5\" >0.9824</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row6_col6\" class=\"data row6 col6\" >0.9826</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row7_col0\" class=\"data row7 col0\" >0.9732</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row7_col1\" class=\"data row7 col1\" >0.0000</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row7_col2\" class=\"data row7 col2\" >0.9333</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row7_col3\" class=\"data row7 col3\" >0.9740</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row7_col4\" class=\"data row7 col4\" >0.9719</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row7_col5\" class=\"data row7 col5\" >0.9235</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row7_col6\" class=\"data row7 col6\" >0.9266</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row8_col0\" class=\"data row8 col0\" >0.9932</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row8_col1\" class=\"data row8 col1\" >0.0000</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row8_col2\" class=\"data row8 col2\" >0.9833</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row8_col3\" class=\"data row8 col3\" >0.9933</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row8_col4\" class=\"data row8 col4\" >0.9932</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row8_col5\" class=\"data row8 col5\" >0.9810</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row8_col6\" class=\"data row8 col6\" >0.9812</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row9_col0\" class=\"data row9 col0\" >1.0000</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row9_col1\" class=\"data row9 col1\" >0.0000</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row9_col2\" class=\"data row9 col2\" >1.0000</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row9_col3\" class=\"data row9 col3\" >1.0000</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row9_col4\" class=\"data row9 col4\" >1.0000</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row9_col5\" class=\"data row9 col5\" >1.0000</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row9_col6\" class=\"data row9 col6\" >1.0000</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row10_col0\" class=\"data row10 col0\" >0.9899</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row10_col1\" class=\"data row10 col1\" >0.0000</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row10_col2\" class=\"data row10 col2\" >0.9784</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row10_col3\" class=\"data row10 col3\" >0.9902</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row10_col4\" class=\"data row10 col4\" >0.9897</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row10_col5\" class=\"data row10 col5\" >0.9721</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row10_col6\" class=\"data row10 col6\" >0.9727</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002level0_row11\" class=\"row_heading level0 row11\" >SD</th>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row11_col0\" class=\"data row11 col0\" >0.0091</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row11_col1\" class=\"data row11 col1\" >0.0000</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row11_col2\" class=\"data row11 col2\" >0.0219</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row11_col3\" class=\"data row11 col3\" >0.0090</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row11_col4\" class=\"data row11 col4\" >0.0095</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row11_col5\" class=\"data row11 col5\" >0.0257</td>\n",
              "                        <td id=\"T_7ffd8eca_d7f6_11ea_84df_0242ac1c0002row11_col6\" class=\"data row11 col6\" >0.0248</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f81a5a90c18>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ov9ni9O_TpsF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "outputId": "7e5e6e5b-664c-431f-bcc0-c1a60c517e75"
      },
      "source": [
        "## Let's now check the model hyperparameters\n",
        "print(xgboost_classifier)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "OneVsRestClassifier(estimator=XGBClassifier(base_score=0.5, booster='gbtree',\n",
            "                                            colsample_bylevel=1,\n",
            "                                            colsample_bynode=1,\n",
            "                                            colsample_bytree=1, gamma=0,\n",
            "                                            learning_rate=0.1, max_delta_step=0,\n",
            "                                            max_depth=3, min_child_weight=1,\n",
            "                                            missing=None, n_estimators=100,\n",
            "                                            n_jobs=-1, nthread=None,\n",
            "                                            objective='binary:logistic',\n",
            "                                            random_state=4471, reg_alpha=0,\n",
            "                                            reg_lambda=1, scale_pos_weight=1,\n",
            "                                            seed=None, silent=None, subsample=1,\n",
            "                                            verbosity=0),\n",
            "                    n_jobs=-1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mdZAVDx2UPfN",
        "colab_type": "text"
      },
      "source": [
        "## Tuning the hyperparametes for better performance"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N2oq1DlCUKVU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273,
          "referenced_widgets": [
            "d73315df6042479586c9f7782ba8490b",
            "92b07214d46b4ae988829aeecd46b921",
            "7b59664e0dc74453889dbcde7ddd4978"
          ]
        },
        "outputId": "05d577e0-1b52-467d-bc5a-f1cec63be28c"
      },
      "source": [
        "# Whenenver we compare different models or build a model, the model uses deault\n",
        "#hyperparameter values. Hence, we need to tune our model to get better performance\n",
        "\n",
        "tuned_xgboost_classifier=tune_model(xgboost_classifier)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style  type=\"text/css\" >\n",
              "    #T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row10_col0 {\n",
              "            background:  yellow;\n",
              "        }    #T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row10_col1 {\n",
              "            background:  yellow;\n",
              "        }    #T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row10_col2 {\n",
              "            background:  yellow;\n",
              "        }    #T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row10_col3 {\n",
              "            background:  yellow;\n",
              "        }    #T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row10_col4 {\n",
              "            background:  yellow;\n",
              "        }    #T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row10_col5 {\n",
              "            background:  yellow;\n",
              "        }    #T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row10_col6 {\n",
              "            background:  yellow;\n",
              "        }</style><table id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Accuracy</th>        <th class=\"col_heading level0 col1\" >AUC</th>        <th class=\"col_heading level0 col2\" >Recall</th>        <th class=\"col_heading level0 col3\" >Prec.</th>        <th class=\"col_heading level0 col4\" >F1</th>        <th class=\"col_heading level0 col5\" >Kappa</th>        <th class=\"col_heading level0 col6\" >MCC</th>    </tr></thead><tbody>\n",
              "                <tr>\n",
              "                        <th id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row0_col0\" class=\"data row0 col0\" >0.9866</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row0_col1\" class=\"data row0 col1\" >0.0000</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row0_col2\" class=\"data row0 col2\" >0.9683</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row0_col3\" class=\"data row0 col3\" >0.9868</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row0_col4\" class=\"data row0 col4\" >0.9863</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row0_col5\" class=\"data row0 col5\" >0.9626</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row0_col6\" class=\"data row0 col6\" >0.9634</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row1_col0\" class=\"data row1 col0\" >1.0000</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row1_col1\" class=\"data row1 col1\" >0.0000</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row1_col2\" class=\"data row1 col2\" >1.0000</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row1_col3\" class=\"data row1 col3\" >1.0000</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row1_col4\" class=\"data row1 col4\" >1.0000</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row1_col5\" class=\"data row1 col5\" >1.0000</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row1_col6\" class=\"data row1 col6\" >1.0000</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row2_col0\" class=\"data row2 col0\" >0.9799</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row2_col1\" class=\"data row2 col1\" >0.0000</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row2_col2\" class=\"data row2 col2\" >0.9524</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row2_col3\" class=\"data row2 col3\" >0.9804</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row2_col4\" class=\"data row2 col4\" >0.9792</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row2_col5\" class=\"data row2 col5\" >0.9432</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row2_col6\" class=\"data row2 col6\" >0.9450</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row3_col0\" class=\"data row3 col0\" >0.9933</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row3_col1\" class=\"data row3 col1\" >0.0000</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row3_col2\" class=\"data row3 col2\" >0.9841</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row3_col3\" class=\"data row3 col3\" >0.9938</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row3_col4\" class=\"data row3 col4\" >0.9933</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row3_col5\" class=\"data row3 col5\" >0.9818</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row3_col6\" class=\"data row3 col6\" >0.9819</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row4_col0\" class=\"data row4 col0\" >1.0000</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row4_col1\" class=\"data row4 col1\" >0.0000</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row4_col2\" class=\"data row4 col2\" >1.0000</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row4_col3\" class=\"data row4 col3\" >1.0000</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row4_col4\" class=\"data row4 col4\" >1.0000</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row4_col5\" class=\"data row4 col5\" >1.0000</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row4_col6\" class=\"data row4 col6\" >1.0000</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row5_col0\" class=\"data row5 col0\" >0.9866</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row5_col1\" class=\"data row5 col1\" >0.0000</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row5_col2\" class=\"data row5 col2\" >0.9812</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row5_col3\" class=\"data row5 col3\" >0.9866</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row5_col4\" class=\"data row5 col4\" >0.9866</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row5_col5\" class=\"data row5 col5\" >0.9644</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row5_col6\" class=\"data row5 col6\" >0.9644</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row6_col0\" class=\"data row6 col0\" >0.9933</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row6_col1\" class=\"data row6 col1\" >0.0000</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row6_col2\" class=\"data row6 col2\" >0.9971</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row6_col3\" class=\"data row6 col3\" >0.9936</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row6_col4\" class=\"data row6 col4\" >0.9934</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row6_col5\" class=\"data row6 col5\" >0.9824</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row6_col6\" class=\"data row6 col6\" >0.9826</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row7_col0\" class=\"data row7 col0\" >0.9799</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row7_col1\" class=\"data row7 col1\" >0.0000</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row7_col2\" class=\"data row7 col2\" >0.9500</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row7_col3\" class=\"data row7 col3\" >0.9804</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row7_col4\" class=\"data row7 col4\" >0.9792</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row7_col5\" class=\"data row7 col5\" >0.9433</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row7_col6\" class=\"data row7 col6\" >0.9451</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row8_col0\" class=\"data row8 col0\" >0.9865</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row8_col1\" class=\"data row8 col1\" >0.0000</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row8_col2\" class=\"data row8 col2\" >0.9667</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row8_col3\" class=\"data row8 col3\" >0.9871</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row8_col4\" class=\"data row8 col4\" >0.9863</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row8_col5\" class=\"data row8 col5\" >0.9621</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row8_col6\" class=\"data row8 col6\" >0.9626</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row9_col0\" class=\"data row9 col0\" >1.0000</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row9_col1\" class=\"data row9 col1\" >0.0000</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row9_col2\" class=\"data row9 col2\" >1.0000</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row9_col3\" class=\"data row9 col3\" >1.0000</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row9_col4\" class=\"data row9 col4\" >1.0000</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row9_col5\" class=\"data row9 col5\" >1.0000</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row9_col6\" class=\"data row9 col6\" >1.0000</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row10_col0\" class=\"data row10 col0\" >0.9906</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row10_col1\" class=\"data row10 col1\" >0.0000</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row10_col2\" class=\"data row10 col2\" >0.9800</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row10_col3\" class=\"data row10 col3\" >0.9909</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row10_col4\" class=\"data row10 col4\" >0.9904</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row10_col5\" class=\"data row10 col5\" >0.9740</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row10_col6\" class=\"data row10 col6\" >0.9745</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002level0_row11\" class=\"row_heading level0 row11\" >SD</th>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row11_col0\" class=\"data row11 col0\" >0.0075</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row11_col1\" class=\"data row11 col1\" >0.0000</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row11_col2\" class=\"data row11 col2\" >0.0187</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row11_col3\" class=\"data row11 col3\" >0.0073</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row11_col4\" class=\"data row11 col4\" >0.0077</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row11_col5\" class=\"data row11 col5\" >0.0210</td>\n",
              "                        <td id=\"T_158e1b6c_d7f7_11ea_84df_0242ac1c0002row11_col6\" class=\"data row11 col6\" >0.0204</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f81a2036cc0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nFWNkrBXU8Dr",
        "colab_type": "text"
      },
      "source": [
        "#### We can clearly conclude that our tuned model has performed better than our original model with default hyperparameters. The mean accuracy increased from 0.9899 to 0.9906\n",
        "\n",
        "#### pycaret library really makes the process of tuning hyperparameters easy\n",
        "#### We just need to pass the model in the following command\n",
        "#### tune_model(model_name)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "47ReN5fjWgyr",
        "colab_type": "text"
      },
      "source": [
        "## Plotting classification plots"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ow6SMWB0Wy4s",
        "colab_type": "text"
      },
      "source": [
        "## Classification Report"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WqjMk3NAWfXf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401,
          "referenced_widgets": [
            "aec1a6ff8c24465d9a90d427594eb11f"
          ]
        },
        "outputId": "e7bf4228-8280-4e92-e2ac-5a92138e7503"
      },
      "source": [
        "plot_model(tuned_xgboost_classifier,plot='class_report')"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "73pMQyL2W3kl",
        "colab_type": "text"
      },
      "source": [
        "## Plotting the confusion matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iEuobdjkUplF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387,
          "referenced_widgets": [
            "6833484a36ba4c289e68a046d6c5f25e"
          ]
        },
        "outputId": "09376b67-9df8-45ca-afc4-70099ffc7f50"
      },
      "source": [
        "plot_model(tuned_xgboost_classifier,plot='confusion_matrix')"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qA09Ng-xYLum",
        "colab_type": "text"
      },
      "source": [
        "## Saving the model for future predictions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DFCJOP3tXDzd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "f9102521-8e3d-49a3-ef63-c3ef325aa917"
      },
      "source": [
        "## This can be used to save our trained model for future use.\n",
        "save_model(tuned_xgboost_classifier,\"XGBOOST CLASSIFIER\")"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Transformation Pipeline and Model Succesfully Saved\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lJTv2sxhYXbr",
        "colab_type": "text"
      },
      "source": [
        "## Loading the saved model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F9JCVRfVYWDK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "e3897b2d-09da-4339-fc2c-0384f6eb4cfa"
      },
      "source": [
        "## This can be used to load our model. We don't need to train our model again and again.\n",
        "saved_model=load_model('XGBOOST CLASSIFIER')"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Transformation Pipeline and Model Sucessfully Loaded\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BrS9puqeYe-r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}